White Matter Hyperintensity Volume and Poststroke Cognition: An Individual Patient Data Pooled Analysis of 9 Ischemic Stroke Cohort Studies

BACKGROUND: White matter hyperintensities (WMH) are associated with cognitive dysfunction after ischemic stroke. Yet, uncertainty remains about affected domains, the role of other preexisting brain injury, and infarct types in the relation between WMH burden and poststroke cognition. We aimed to disentangle these factors in a large sample of patients with ischemic stroke from different cohorts. METHODS: We pooled and harmonized individual patient data (n=1568) from 9 cohorts, through the Meta VCI Map consortium (www.metavcimap.org). Included cohorts comprised patients with available magnetic resonance imaging and multidomain cognitive assessment <15 months poststroke. In this individual patient data meta-analysis, linear mixed models were used to determine the association between WMH volume and domain-specific cognitive functioning (Z scores; attention and executive functioning, processing speed, language and verbal memory) for the total sample and stratified by infarct type. Preexisting brain injury was accounted for in the multivariable models and all analyses were corrected for the study site as a random effect. RESULTS: In the total sample (67 years [SD, 11.5], 40% female), we found a dose-dependent inverse relationship between WMH volume and poststroke cognitive functioning across all 4 cognitive domains (coefficients ranging from −0.09 [SE, 0.04, P=0.01] for verbal memory to −0.19 [SE, 0.03, P<0.001] for attention and executive functioning). This relation was independent of acute infarct volume and the presence of lacunes and old infarcts. In stratified analyses, the relation between WMH volume and domain-specific functioning was also largely independent of infarct type. CONCLUSIONS: In patients with ischemic stroke, increasing WMH volume is independently associated with worse cognitive functioning across all major domains, regardless of old ischemic lesions and infarct type.


Image processing WMH segmentations
For eight cohorts WMH segmentations were performed in Utrecht as part of the current project, using open access fully automated techniques. 12The best segmentation method was selected on a per cohort basis after visual inspection of the segmentation results.For six cohorts (i.e.Bundang VCI, CODECS, DEDEMAS, Hallym VCI, PROCRAS, and STROKDEM) the coroflo segmentation method was selected 34 whereas for COAST the bigrbrain segmentation method was selected. 35or USCOG it was not possible to select a segmentation method on a per cohort basis due to heterogeneity of the imaging data and differences in quality of the different segmentation methods per patient.Therefore, selection of the segmentation method for USCOG was performed on a per subject basis.An expert (MC) with extensive experience on WMH segmentations visually inspected all segmentations.Scans with major disturbances due to technical issues such as scan quality or movement artefacts, or due to old infarcts or other pathology with apparent impact on WMH segmentations and thus volume estimates of an individual patient, were excluded.The acute infarct segmentations were subtracted from the WMH maps at a subsequent processing step.In total 93 (5.3%) of all segmentations failed and were excluded.For CASPER WMH segmentations were provided by the participating center, details are described elsewhere. 13

Registration of WMH segmentations to the MNI-152 template
The registration of WMH segmentations to the MNI-152 brain template was performed centrally using RegLSM. 15The FLAIR images were first registered to the corresponding T1 image with a linear registration.The T1 image was subsequently transformed to the T1 1-mm MNI-152 template, with a linear registration followed by a non-linear registration.An age-specific MRI template was used as an intermediate step before the final registration to MNI-152 space in order to improve the quality of the registration by providing a better match between patient and template. 14The resulting transformations were combined into a single transformation that was subsequently used to transform the corresponding WMH map to the MNI-152 template.The final registration results of all cases were visually checked for accuracy and 89 patients (5.3%) with failed registrations were excluded.To reduce heterogeneity and minimize the effects of possible misclassifications of other lesion types as WMH during the WMH segmentation procedures, voxels located outside the white matter (defined using the MNI probabilistic white matter atlas thresholded 14 at 30%) were removed from all individual WMH maps.As a final processing step, lesion maps of the acute infarct were subtracted from the WMH maps.

Lacune and old infarct ratings
Lacunes were identified visually, using STRIVE criteria. 16In short, a lacune was defined as a round or ovoid, subcortical, fluid-filled cavity (signal similar to CSF) of between 3 mm and 15 mm in diameter.Lacunes were identified on the T1 sequence, with the FLAIR sequence and segmentations of the acute infarct used as reference in all cases.For visual identification of old infarcts (i.e.cortical and cerebellar infarcts and subcortical infarcts too large to meet criteria for lacunes), FLAIR and T1 sequences and the segmentation of the acute infarct were visualized simultaneously.Lesions were classified as old infarcts based on the following characteristics: 1) gliosis (FLAIR), 2) volume loss due to infarction (T1) 3) damaged (cortical) tissue (both FLAIR/T1) 4) lesion not included in the yet available acute infarct segmentation 5) lesion did not meet the criteria of a lacune.All scans were screened for lacunes and old infarcts by two independent raters (FK and JMB or FK and GJB).In case of disagreement between ratings, consensus meetings were held.The scans of the CASPER cohort were already rated using the same criteria.

Atrophy measures
For all cohorts, the brain parenchymal fraction (BPF; total grey matter volume + total white matter volume divided by intracranial volume) was computed using the Computational Anatomy Toolbox (CAT) for SPM12. 36For Hallym VCI and Bundang VCI, CAT for SPM12 did not generate reliable results, likely because of difficulty identifying the CSF/skull interface, therefore failing to measure ICV.FMRIB's Software Library (FSL) 37 was therefore used as an alternative method to compute BPF for these cohorts, but after visual inspection these segmentations did also not meet our quality control standards for the CSF spaces, including ventricles, and brain parenchyma.Consequently, Hallym VCI and Bundang VCI (n=1080) were excluded for the analyses that involved brain atrophy.

Cognitive data processing and harmonization Selection of neuropsychological tests
To reliably compare individual performance (z-scores) on cognitive domains, heterogeneity between cohorts was minimized by only selecting neuropsychological tests that were available in at least 40% of cohorts (selected tests were either truly identical or equivalent in both difficulty and cognitive construct measured).This process resulted in the selection of the following neuropsychological tests: TMT B, Digit Span Forward, Digit Span Backward, Phonemic Fluency (both 2 and 3 letter tests), Semantic Fluency (animal naming), TMT A, WAIS-R Digit Symbol Substitution Test (equivalent: Symbol Digit Modalities test) and the Boston Naming Test (equivalent: French D080 picture naming test).For verbal memory we included all word list recall tests measuring at least two of the following constructs: immediate recall, delayed recall and recognition (Rey Auditory Verbal Learning Test, Seoul Verbal Learning test, Word-List Recall, Free and Cued Selective Reminding Test and, the Word List Memory Task).Allocation of tests to specific cognitive domains was based on previous work. 7Table S1 shows the selection of neuropsychological tests for each cohort.

Norm-referenced data
Cognitive performance at the level of individual neuropsychological tests was determined using local norms or normative data (corrected for age, educational level and sex where appropriate).Z-scores were calculated by the Utrecht team for the CODECS and USCOG cohorts, all other cohorts provided norm-referenced percentile scores or z-scores for each individual test.Percentile scores were converted to z-scores accordingly.Normative data for neuropsychological assessment per cohort are described in the supplements of prior work. 7In addition, for DEDEMAS, the following norm-data were used: (1) z-scores of CERAD test battery 38 (including TMT B, Phonemic fluency, TMT A, Boston Naming Test, Semantic fluency-animals, Word-List Memory Task) were based on published norms using a standardized program (2) Z-scores of Digit Symbol Coding were calculated based on normative scores of Wechsler Adult Intelligence Scale, Third Edition (WAIS-III). 39

Identification of outliers and construction of cognitive domain z-scores
Extreme scores were defined as the mean z-score of an individual test +/-3SD, on a per cohort basis.Extreme scores differed per test and per cohort and are probably the result of a combination of specific test characteristics (i.e.time-related tasks are more prone to generating extreme scores), patient characteristics (i.e.floor effects) and norm characteristics, that all differ per cohort.To reduce the impact of these (likely exaggerated) extreme z-scores, all extreme scores were set back to the cut-off value of the mean of the individual test +/-3SD for each individual cohort.The final zscores of individual tests were used to calculate cognitive domain z-scores (mean of all available z-scores within one domain).History of stroke or TIA 79 (14.5) 6 (6.0) 0 (0) 0 ( 0 -0.9 (1.1)# -0.7 (0.7) -0.4 (1.8) -1.0 (0.8) -0.1 (0.9) -0.7 (1.1)|| -0.7 (0.8) § -0.8 (1.6)|| -0.8 (0.6) -0.7 (1.0) Processing speed, zscores, mean (SD) -1.1 (1.2) § -0.4 (0.8)# -1.4 (1.5) -0.6 (1.0) 0.0 (0.8) -0.5 (0.9) -1.0 (0.9)|| -0.2 (0.7) § -1.0 (1.3) -0.7 (1.1) Verbal memory, z-scores, mean (SD)   (0-3)  2 (1-4)  2 (1-4)  2 (1-4)  2 (1-5)  2 (1-4)  2 (1-5)  3 (1-4)  3 (1-5)  3 (1-5)  2 (1-4 This sensitivity analysis was done to assess if having a strategic infarct (i.e. a lesion location with high risk of developing PSCI) would alter the relation (i.e.effect sizes) between WMH volume and poststroke cognitive functioning.Details on the location impact score, that provides risk estimates for the occurrence of PSCI according to infarct location, are described in prior work. 7In short, using the acute infarct segmentations, voxel based lesion symptom mapping results (to relate infarct location to PSCI occurrence) were used to calculate this score for each individual participant: the location impact score is the mean coefficient (ie, ln[OR]) of voxels of the patient's acute infarct.Infarcts with high location impact scores are seen as strategic infarcts, the higher the score the greater risk of PSCI.For this analysis, all patients with availability of the location impact score from the Meta VCI Map strategic infarct location study 7 were included (n=1502).These patients were stratified into tertiles, based on their continuous location impact score.We did not use the original 5-point scale due to sample size constraints of subgroups.Stratified analyses were used because the risk of poor cognitive outcome (PSCI) is integrated in this score and it can therefore not be added as co-variate in the models.Results showed that the effect sizes for the relation between WMH volume and cognition was highest in the tertile with the highest location impact scores and lowest for the tertile with the lowest scores.

Figure
Figure S1.Examples of white matter hyperintensity and acute infarct lesion maps on the MNI-152 template Panel A,B: Two examples of white matter hyperintensity lesion maps (in red) and corresponding acute infarct lesion maps (in green) at the same transversal slice.Panel C: Example of a white matter hyperintensity lesion map (in red) and the corresponding infarct lesion map (in green) at different slices.The infarcts shown at panel A and C are examples of the large infarct-type.The infarct shown at Panel B is an example of a small subcortical infarct-type.

Table S1 . Selection of cognitive tests per cohort
Standard operating procedures were followed to ensure that the fully processed lesion data matched the original imaging data and the clinical dataset provided by the participating center.These quality control procedures are complementary to procedures previously described by Weaver et al.7For each cohort, the Utrecht team